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  • 1
    Online Resource
    Online Resource
    Standard Error of Measurement of 5 Health Utility Indexes across the Range of Health for Use in Estimating Reliability and Responsiveness
    SAGE Publications ; 2011
    In:  Medical Decision Making Vol. 31, No. 2 ( 2011-03), p. 260-269
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 31, No. 2 ( 2011-03), p. 260-269
    Abstract: Background. Standard errors of measurement (SEMs) of health-related quality of life (HRQoL) indexes are not well characterized. SEM is needed to estimate responsiveness statistics, and is a component of reliability. Purpose. To estimate the SEM of 5 HRQoL indexes. Design. The National Health Measurement Study (NHMS) was a population-based survey. The Clinical Outcomes and Measurement of Health Study (COMHS) provided repeated measures. Subjects. A total of 3844 randomly selected adults from the noninstitutionalized population aged 35 to 89 y in the contiguous United States and 265 cataract patients. Measurements. The SF6-36v2™, QWB-SA, EQ-5D, HUI2, and HUI3 were included. An item-response theory approach captured joint variation in indexes into a composite construct of health (theta). The authors estimated 1) the test-retest standard deviation (SEM-TR) from COMHS, 2) the structural standard deviation (SEM-S) around theta from NHMS, and 3) reliability coefficients. Results. SEM-TR was 0.068 (SF-6D), 0.087 (QWB-SA), 0.093 (EQ-5D), 0.100 (HUI2), and 0.134 (HUI3), whereas SEM-S was 0.071, 0.094, 0.084, 0.074, and 0.117, respectively. These yield reliability coefficients 0.66 (COMHS) and 0.71 (NHMS) for SF-6D, 0.59 and 0.64 for QWB-SA, 0.61 and 0.70 for EQ-5D, 0.64 and 0.80 for HUI2, and 0.75 and 0.77 for HUI3, respectively. The SEM varied across levels of health, especially for HUI2, HUI3, and EQ-5D, and was influenced by ceiling effects. Limitations. Repeated measures were 5 mo apart, and estimated theta contained measurement error. Conclusions. The 2 types of SEM are similar and substantial for all the indexes and vary across health.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X10380925
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2011
    detail.hit.zdb_id: 2040405-0
    detail.hit.zdb_id: 604497-9
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  • 2
    Online Resource
    Online Resource
    Agreement about Identifying Patients Who Change over Time : Cautionary Results in Cataract and Heart Failure Patients
    SAGE Publications ; 2012
    In:  Medical Decision Making Vol. 32, No. 2 ( 2012-03), p. 273-286
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 32, No. 2 ( 2012-03), p. 273-286
    Abstract: Background. Preference-based measures of health-related quality of life all use the same dead = 0.00 to perfect health = 1.00 scale, but there are substantial differences among measures. Objective. The objective was to examine agreement in classifying patients as better, stable, or worse. Methods. The EQ-5D, Health Utilities Index Mark 2 and Mark 3, Quality of Well-Being–Self-Administered scale, Short-Form 36 (Short-Form 6D), and disease-targeted measures were administered prospectively in 2 clinical cohorts. The study was conducted at academic medical centers: University of California, Los Angeles; University of California, San Diego; University of Wisconsin–Madison; and University of Southern California. Patients undergoing cataract extraction surgery with lens replacement completed the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25). Patients newly referred to congestive heart failure specialty clinics completed the Minnesota Living with Heart Failure Questionnaire (MLHF). In both cohorts, subjects completed surveys at baseline and at 1 and 6 months. The NEI-VFQ-25 and MLHF were used as gold standards to assign patients to categories of change. Agreement was assessed using κ. Results. There were 376 cataract patients recruited. Complete data for baseline and the 1-month follow-up were available on all measures for 210 cases. Using criteria specified by Altman, agreement was poor for 6 of 9 pairs of comparisons and fair for 3 pairs. There were 160 heart failure patients recruited. Complete data for baseline and the 6-month follow-up were available for 86 cases. Agreement was negligible for 5 pairs and fair for 1. The study was conducted on selected patients at a few academic medical centers. Conclusions. The results underscore the lack of interchangeability among different preference-based measures.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X11418671
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2012
    detail.hit.zdb_id: 2040405-0
    detail.hit.zdb_id: 604497-9
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  • 3
    Online Resource
    Online Resource
    Health Condition Impacts in a Nationally Representative Cross-Sectional Survey Vary Substantially by Preference-Based Health Index
    SAGE Publications ; 2016
    In:  Medical Decision Making Vol. 36, No. 2 ( 2016-02), p. 264-274
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 36, No. 2 ( 2016-02), p. 264-274
    Abstract: Importance: Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0. Objective: We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale. Design: Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States. Main Outcome Measures: The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years. Results: There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., 〉 0.69). Limitations: This analysis uses cross-sectional data and lacks health condition severity information. Conclusions: Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X15599546
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
    detail.hit.zdb_id: 2040405-0
    detail.hit.zdb_id: 604497-9
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  • 4
    Online Resource
    Online Resource
    Comparison of 5 Health-Related Quality-of-Life Indexes Using Item Response Theory Analysis
    SAGE Publications ; 2010
    In:  Medical Decision Making Vol. 30, No. 1 ( 2010-01), p. 5-15
    Details
    In: Medical Decision Making, SAGE Publications, Vol. 30, No. 1 ( 2010-01), p. 5-15
    Abstract: Background. Five health-related quality-of-life (HRQoL) indexes—EQ-5D, HUI2, HUI3, QWB-SA, and SF-6D—are each used to assign community-based utility scores to health states, although these scores differ. Objective. The authors transform these indexes to a common scale to understand their interrelationships. Methods. Data were from the National Health Measurement Study, a telephone survey of 3844 US adults. The 5 indexes were analyzed using item response theory analysis to estimate scores on an underlying construct of summary health, θ. Unidimensionality was evaluated using nonlinear principal components analysis. Index scores were plotted against the estimated scores on the common underlying construct. In addition, scores on the Health and Activities Limitation Index (HALex), the Centers for Disease Control and Prevention Healthy Days questions, and self-rated health on a 5-category scale ranging from excellent to poor were plotted. Results. SF-6D and QWB-SA are nearly linear across the range of θ but with a shallow slope; EQ-5D, HUI2, and HUI3 are linear with a steep slope from low θ (poor health) into midrange of θ, then approximately linear with a less steep slope for higher θ (health just below to well above average), although the inflection points differ by index. Conclusion. Simple linear functions may serve as crosswalks among these indexes only for lower health states, albeit with low precision. Ceiling effects make crosswalks among most of the indexes ill specified above a certain level of health. Although each index measures generic health on a utility scale, these indexes are not identical but are relatively simply, if imprecisely, related.
    Type of Medium: Online Resource
    ISSN: 0272-989X , 1552-681X
    URL: Article
    DOI: 10.1177/0272989X09347016
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 2040405-0
    detail.hit.zdb_id: 604497-9
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  • 5
    Online Resource
    Online Resource
    Division of Primary Care Services Between Physicians, Physician Assistants, and Nurse Practitioners for Older Patients With Diabetes
    SAGE Publications ; 2013
    In:  Medical Care Research and Review Vol. 70, No. 5 ( 2013-10), p. 531-541
    Details
    In: Medical Care Research and Review, SAGE Publications, Vol. 70, No. 5 ( 2013-10), p. 531-541
    Abstract: Team-based care involving physician assistants and/or nurse practitioners (PA/NPs) in the patient-centered medical home is one approach to improving care quality. However, little is known about how to incorporate PA/NPs into primary care teams. Using data from a large physician group, we describe the division of patients and services (e.g., acute, chronic, preventive, other) between primary care providers for older diabetes patients on panels with varying levels of PA/NP involvement (i.e., no role, supplemental provider, or usual provider of care). Panels with PA/NP usual providers had higher proportions of patients with Medicaid, disability, and depression. Patients with physician usual providers had similar probabilities of visits with supplemental PA/NPs and physicians for all service types. However, patients with PA/NP usual providers had higher probabilities of visits with a supplemental physician. Understanding how patients and services are divided between PA/NPs and physicians will assist in defining provider roles on primary care teams.
    Type of Medium: Online Resource
    ISSN: 1077-5587 , 1552-6801
    URL: Article
    DOI: 10.1177/1077558713495453
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2013
    detail.hit.zdb_id: 2070248-6
    detail.hit.zdb_id: 1232314-7
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